Given a document, the goal of single document summarization is to produce a shortened summary of the document that captures its main semantic content [1]}. Existing research extends over several genres, including news [2]}, [3]}, [4]}, scientific writing [5]}, [6]}, [7]}, legal case reports  [8]}, etc. A critical factor in successful summarization research is the availability of a dataset with parallel document/human-summary pairs for system evaluation. However, no such publicly available resource for summarization of contracts exists to date. We present the first dataset in this genre.
Note that unlike other genres where human summaries paired with original documents can be found at scale, e.g., the CNN/DailyMail dataset [3]}, resources of this kind are yet to be curated/created for contracts. As traditional supervised summarization systems require these types of large datasets, the resources released here are intended for evaluation, rather than training. Additionally, as a first step, we restrict our initial experiments to unsupervised baselines which do not require training on large datasets.
